This function computes the Homophily Configuration Graph type estimates for a categorical variable.
RDS.HCG.estimates(
rds.data,
outcome.variable,
N = NULL,
subset = NULL,
small.fraction = FALSE,
empir.lik = TRUE,
to.factor = FALSE,
cont.breaks = 3
)
If the empir.lik
is true, an object of class
rds.interval.estimate
is returned. This is a list with components
estimate
: The numerical point estimate of proportion
of the trait.variable
.
interval
: A matrix with six
columns and one row per category of trait.variable
:
point estimate
: The HT estimate of the population mean.
95% Lower Bound
: Lower 95% confidence bound.
95%
Upper Bound
: Upper 95% confidence bound.
Design Effect
: The
design effect of the RDS.
s.e.
: Standard error.
n
:
Count of the number of sample values with that value of the trait.
Otherwise an object of class rds.HCG.estimate
object is returned.
An rds.data.frame
with recruitment time set.
A string giving the name of the variable in the
rds.data
that contains a categorical variable to be analyzed.
Population size to be used to calculate the empirical likelihood interval. If NULL, this value is taken to be the population.size.mid attribute of the data and if that is not set, no finite population correction is used.
An optional criterion to subset rds.data
by. It is
an R expression which, when evaluated, subset the
data. In plain English, it can be something like subset = seed > 0
to
exclude seeds. It can also be the name of a logical vector of the same length of
the outcome variable where TRUE means include it in the analysis. If
NULL
then no subsetting is done.
Should a small sample fraction be assumed
Should confidence intervals be estimated using empirical likelihood.
force variable to be a factor
If variable is numeric, how many discretization points should be used in the calculation of the weights.
Ian E. Fellows
RDS.I.estimates
, RDS.II.estimates
, RDS.SS.estimates
data(fauxtime)
RDS.HCG.estimates(rds.data=fauxtime,outcome.variable='var1')
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